{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Inferential Statistics\n",
    "\n",
    "1. Inferential Statistics \n",
    "2. Sampling Techniques  \n",
    "    a. Convenience Sampling  \n",
    "    b. Systematic Sampling  \n",
    "    c. Simple Random Sampling  \n",
    "    d. Stratified Sampling  \n",
    "    e. Cluster Sampling  \n",
    "4. Population Parameter vs Sample Statistic\n",
    "5. How to perform Estimations/Approximations/Inference?\n",
    "6. Sampling in Pandas DataFrame\n",
    "7. Point Estimate using 1 Sample\n",
    "    - How large a sample should be?\n",
    "8. Central Limit Theorem\n",
    "    - Point Estimate using m Sample\n",
    "    - Confidance Interval Estimates using 1 Sample (if Population Std is known)\n",
    "    - Confidance Interval Estimates using 1 Sample (if Population Std is unknown)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inferential Statistics\n",
    "\n",
    "<img style=\"float: right;\" width=\"600\" height=\"400\" src=\"images/01_inferential_stats.PNG\">\n",
    "Idea is to estimate the Population's PARAMETERS using Sample's Statistics.\n",
    "\n",
    "Inferential statistics involves making inferences or predictions about a population based on a sample of data. It's used when it's not feasible or practical to study an entire population, so we use sample data to draw conclusions about the larger population.\n",
    "\n",
    "Key concepts in inferential statistics include confidence intervals, hypothesis testing, and regression analysis. It helps us make educated guesses or predictions about a population parameter based on sample data.\n",
    "\n",
    "The goal is to leverage data in real time to make informed decisions, optimize processes, and enhance overall business performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling Techniques  \n",
    "Sampling techniques are methods used to select a subset of individuals or items from a larger population for the purpose of making inferences about the entire population. The choice of sampling technique depends on the research objectives, resources available, and the characteristics of the population being studied. Each technique has its own strengths and limitations in terms of representativeness, precision, and practicality. Here are various sampling techniques:\n",
    "\n",
    "1. **Convenience Sampling:**\n",
    "   - Individuals are selected based on their availability and accessibility. This method is convenient but may not be representative of the population. Example: Conduct interviews with people in a shopping mall to gather opinions on a new product.\n",
    "\n",
    "2. **Systematic Sampling:**\n",
    "   - Individuals or items are selected at regular intervals from an ordered list. For example, every 5th item is selected.\n",
    "\n",
    "3. **Simple Random Sampling (SRS):**\n",
    "   - In SRS, every individual or item in the population has an equal chance of being selected. This is done **without replacement**. Example: Assign a unique number to each student in a school, and then use a random number generator to select 50 students from the list.\n",
    "\n",
    "4. **Stratified Sampling:**\n",
    "   - The population is divided into distinct strata (subgroups) based on certain characteristics. Then, random samples are drawn from each stratum proportionally. Divide a city's population into age groups (e.g., 0-18, 19-35, 36-60, 60+), and then randomly select a proportional number of individuals from each group.\n",
    "\n",
    "5. **Cluster Sampling:**\n",
    "   - The population is divided into clusters, and a random sample of clusters is selected. Then, all individuals within the selected clusters are included in the sample. Example: Divide a country into regions, randomly select a few regions, and then survey all households within those chosen regions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Population Parameter and Sample Statistics\n",
    "\n",
    "**Population Parameter:**  \n",
    "A population parameter is a numerical characteristic of an entire population. It describes some aspect of the population's distribution or behavior. Examples of population parameters include the population mean, population standard deviation, population proportion, etc.\n",
    "For example, if you're interested in the average height of all adult males in a country, the actual average height of the entire population is the population parameter.\n",
    "\n",
    "**Sample Statistics:**  \n",
    "Sample statistics are numerical measures calculated from a sample (subset) of the population. They are used to estimate or infer information about the corresponding population parameter. Common sample statistics include the sample mean, sample standard deviation, sample proportion, etc.\n",
    "Using the previous example, if you select a group of 100 adult males from that country and calculate their average height, that calculated value is a sample statistic. This sample statistic is an estimate of the population parameter (average height of all adult males).\n",
    "\n",
    "1. **Population Mean (μ) and Sample Mean (x̄):**\n",
    "   - Population Mean (μ): $ \\mu = \\frac{\\sum x}{N} $\n",
    "   - Sample Mean (x̄): $ x̄ = \\frac{\\sum x}{n} $\n",
    "\n",
    "2. **Population Variance (σ²) and Sample Variance (s²):**\n",
    "   - Population Variance (σ²): $ \\sigma² = \\frac{\\sum (x - \\mu)²}{N} $\n",
    "   - Sample Variance (s²): $ s² = \\frac{\\sum (x - x̄)²}{n-1} $ (Note: Bessel's correction is applied for sample variance)\n",
    "\n",
    "3. **Population Standard Deviation (σ) and Sample Standard Deviation (s):**\n",
    "   - Population Standard Deviation (σ): $ \\sigma = \\sqrt{\\sigma²} $\n",
    "   - Sample Standard Deviation (s): $ s = \\sqrt{s²} $\n",
    "\n",
    "**Bessel's Correction**  \n",
    "Bessel's correction is a mathematical adjustment made to sample statistics to provide unbiased estimates of population parameters. It's particularly important when calculating the sample variance and sample standard deviation.\n",
    "\n",
    "The main utility of Bessel's correction lies in correcting the bias that can occur when estimating population parameters from a sample. Without this correction, sample statistics can tend to underestimate the true population parameters.\n",
    "\n",
    "In summary, Bessel's correction is a fundamental adjustment in statistics that helps to provide more accurate estimates of population parameters based on sample data. It ensures that sample statistics, particularly the sample variance, are unbiased estimators of their corresponding population parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to perform Estimations/Approximations/Inference?\n",
    "\n",
    "**Step 1 -** Take a Sample. Uniformly at random try to take a large enough sample from population such that the sample is a correct representation of the entire population.  \n",
    "**Step 2 -** Calculate the Sample Statistics  \n",
    "**Step 3 -** Estimate the Population Parameter using the Sample Statistics. There are various estimation techniques like Point Estimate, Confidence Interval Estimate, Maximum Likelyhood Estimation, GMM's, etc...\n",
    "\n",
    "**Note that:**\n",
    "1. Sample should be sufficiently large.\n",
    "    - **Why?** So that it start representing the population  \n",
    "2. Sample should be a correct representaion of the population.\n",
    "    - **Why?** So that you can make unbiased estimates \n",
    "3. Sample should be unbiased (choose better sampling technique)\n",
    "\n",
    "**Bias (i.e. Error)**\n",
    "1. Sampling Bias - Can be resolved by choosing a better sampling process\n",
    "2. Outlier Bias - Can be resolved by taking a sufficiently large enough sample or using confidence interval estimate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
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       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass  fnlwgt     education  educational-num      marital-status  \\\n",
       "0   25    Private  226802          11th                7       Never-married   \n",
       "1   38    Private   89814       HS-grad                9  Married-civ-spouse   \n",
       "2   28  Local-gov  336951    Assoc-acdm               12  Married-civ-spouse   \n",
       "3   44    Private  160323  Some-college               10  Married-civ-spouse   \n",
       "4   18          ?  103497  Some-college               10       Never-married   \n",
       "\n",
       "          occupation relationship   race  gender  capital-gain  capital-loss  \\\n",
       "0  Machine-op-inspct    Own-child  Black    Male             0             0   \n",
       "1    Farming-fishing      Husband  White    Male             0             0   \n",
       "2    Protective-serv      Husband  White    Male             0             0   \n",
       "3  Machine-op-inspct      Husband  Black    Male          7688             0   \n",
       "4                  ?    Own-child  White  Female             0             0   \n",
       "\n",
       "   hours-per-week native-country income  \n",
       "0              40  United-States  <=50K  \n",
       "1              50  United-States  <=50K  \n",
       "2              40  United-States   >50K  \n",
       "3              40  United-States   >50K  \n",
       "4              30  United-States  <=50K  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_df = pd.read_csv('data/adult.csv')\n",
    "\n",
    "population_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48842, 15)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48842.000000</td>\n",
       "      <td>4.884200e+04</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.643585</td>\n",
       "      <td>1.896641e+05</td>\n",
       "      <td>10.078089</td>\n",
       "      <td>1079.067626</td>\n",
       "      <td>87.502314</td>\n",
       "      <td>40.422382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.710510</td>\n",
       "      <td>1.056040e+05</td>\n",
       "      <td>2.570973</td>\n",
       "      <td>7452.019058</td>\n",
       "      <td>403.004552</td>\n",
       "      <td>12.391444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.175505e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.781445e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.376420e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.490400e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        fnlwgt  educational-num  capital-gain  \\\n",
       "count  48842.000000  4.884200e+04     48842.000000  48842.000000   \n",
       "mean      38.643585  1.896641e+05        10.078089   1079.067626   \n",
       "std       13.710510  1.056040e+05         2.570973   7452.019058   \n",
       "min       17.000000  1.228500e+04         1.000000      0.000000   \n",
       "25%       28.000000  1.175505e+05         9.000000      0.000000   \n",
       "50%       37.000000  1.781445e+05        10.000000      0.000000   \n",
       "75%       48.000000  2.376420e+05        12.000000      0.000000   \n",
       "max       90.000000  1.490400e+06        16.000000  99999.000000   \n",
       "\n",
       "       capital-loss  hours-per-week  \n",
       "count  48842.000000    48842.000000  \n",
       "mean      87.502314       40.422382  \n",
       "std      403.004552       12.391444  \n",
       "min        0.000000        1.000000  \n",
       "25%        0.000000       40.000000  \n",
       "50%        0.000000       40.000000  \n",
       "75%        0.000000       45.000000  \n",
       "max     4356.000000       99.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48842 entries, 0 to 48841\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   age              48842 non-null  int64 \n",
      " 1   workclass        48842 non-null  object\n",
      " 2   fnlwgt           48842 non-null  int64 \n",
      " 3   education        48842 non-null  object\n",
      " 4   educational-num  48842 non-null  int64 \n",
      " 5   marital-status   48842 non-null  object\n",
      " 6   occupation       48842 non-null  object\n",
      " 7   relationship     48842 non-null  object\n",
      " 8   race             48842 non-null  object\n",
      " 9   gender           48842 non-null  object\n",
      " 10  capital-gain     48842 non-null  int64 \n",
      " 11  capital-loss     48842 non-null  int64 \n",
      " 12  hours-per-week   48842 non-null  int64 \n",
      " 13  native-country   48842 non-null  object\n",
      " 14  income           48842 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 5.6+ MB\n"
     ]
    }
   ],
   "source": [
    "population_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RNT00ERERcSCmXjL7IyNHjrT/uWPHjnTq1InmzZuzYsUKBgwYUGvv++STTzJp0iT71/n5+QpFIiIiTsyhzxD9XlxcHCEhIezbtw+AiIgIMjIyqvSpqKggOzvbPu8oIiKC9PT0Kn1Of322uUleXl74+/tXeYiIiIjzqleB6MiRI2RlZREZGQlAfHw8ubm5bN682d5n2bJl2Gw2evfube/z008/UV5ebu+zZMkSWrduTaNGjep2ACIiIuKQTA1EBQUFJCUlkZSUBEBKSgpJSUmkpqZSUFDAY489xrp16zh48CAJCQlcf/31tGjRgsGDBwPQtm1bhgwZwr333suGDRtYs2YNEyZMYOTIkURFRQFw++234+npybhx49ixYwdz587lrbfeqnJJTERERFybqYFo06ZNdO3ala5duwIwadIkunbtypQpU3Bzc2Pr1q0MHz6cVq1aMW7cOLp3786qVavw8vKyv8acOXNo06YNAwYM4JprrqFfv35V9hgKCAhg8eLFpKSk0L17dx555BGmTJmiJfciIiJiZ+qk6v79+2MYxlnbFy1a9IevERQUxGeffXbOPp06dWLVqlUXXF9duXXUaDKycqttCwsOZO6cT+u2IBERERfj0KvMXEVGVi7DH3m12rZ5rz1ax9VIdYrKKjiUVUROYRm5xeUYBrhZLfj7uBPdqAERAd54uNWrKXkiIvIbFxWI4uLi2LhxI8HBwVWO5+bm0q1bNw4cOFAjxYmY4WhuMZsOZrPr+En2pp9kT8ZJjuQUc46TmXi6WekSHUjvuCCGdIigXaQ/Foul7ooWEZFLclGB6ODBg/bNEX+rtLSUo0ePXnJRInXFZjNITj/JhpRsNh7MZvOhHI7nlVTbt1EDD0J8vQjw8cDNaqHCZpBTWMaR3GLKKmxsOJjNhoPZvLNsH63D/RjVpym39IjG28OtjkclIiIX6oIC0bx58+x/XrRoEQEBAfavKysrSUhIoFmzZjVWnEhNMwyDfRkFJB7IInF/FutTsskuLKvSx81qoX2UPx0bB9A6wo+WYX60Cvcl2Ner2te02QwOZhWyISWblXsySdiVQXL6SaZ8t4O3E/Zx/5Vx3Bkfg5e7gpGIiKO6oEB0ww03AGCxWBgzZkyVNg8PD5o1a8Zrr71WY8WJXCrDMDhwopDE/VmsO5DFugPZnCgordKngacb3WMa0atZEN2bNaJLdCANPM//fw2r1UJcqC9xob6M7NWUvKJyvvnlCB+sSuFobjF/X7CL2esO8cy17RjQNvyPX1BEROrcBQUim80GQGxsLBs3biQkJKRWihK5FCXllSTuz2LZ7gyW7c7gaG5xlXYvdys9mjUiPi6Y+ObBdGoSWKMTogMaeHDXZbHc3juGr38+wquL93Awq4hxn2xieOconr++PYENPP/4hUREpM5c1ByilJSUmq5D5JLYrB58/fMRfth2nNX7TlBSbrO3ebpZ6do0kPjmwcTHBdOlaWCdXL7ydLcysldThnWK5J1l+/hodQrzthxjfUoWL43oRP/WYX/8IiIiUicuetl9QkICCQkJZGRk2M8cnfbxxx9fcmEif6S80saBzEL2ZpwktcNoJn25xd4WGeDNVW3CuLp1GH1bBF/QJbCa5uftwd+uacvQDhE88tUWDmQWctfMjdzeuylPXdOWhl7a/UJExGwX9Z34ueee4/nnn6dHjx5ERkZqebHUGcMwSMsvYcexfPamF1BW+WsYt7oTF9qQaztFMaR9BG0j/Rzu32XXpo1Y8ODlvLxoNzPXHOSz9ams2XeCN27tQremuq+eiIiZLioQzZgxg1mzZnHnnXfWdD0i1co8WUpeaCdmr0slu+h/q8L8vd1pFe7HsUXvkzBtpsOFoN/z8XRj6nXt+VO7cB77aiuHsoq4eUYiE65qwYNXt8BdmzuKiJjiogJRWVkZffv2relaRKqw2QxW7TvBnHWHWLY7g4rG8VBUhrvVQsswX9pF+dM40AeLxcK877IdPgz9Vt/mIfzw0OVM/W473yYd462Evazck8mbt3ahWUhDs8sTEXE5F/Xr6D333POH9w8TuVgnS8qZtSaFga+vZMzHG1i8M50Km4FXYToD2oRxz+WxDGofQZNGDepVCPq9AB8P3hzZlbdGdsHP252kw7lc8/Yq5m5MPec9/kREpOZd1BmikpIS3n//fZYuXUqnTp3w8PCo0v7666/XSHHiWg5lFTJzzUH+s/kIBaUVAPh5ufPnHk24rVdT/m/MKDoM72dylTXv+i6N6dEsiEe+TGLdgWwm/3cby3Zn8I8bO551M0gREalZFxWItm7dSpcuXQDYvn17lbb6/Bu71D3DMNh4MIcPVx1gya50+/3Cmoc25K6+zbixWxN8XWAVVuNAH+bc04cPVh3gtcXJLNqRzroD2Tw+pDUjezbFzar/r0REatNF/aRZvnx5TdchLqa80sYP247z0eoUth7Jsx/v3zqUuy+L5fKWIfUuXN86ajQZWbnVtoUFBzJ3zqfnfL6b1cL9VzanX4sQHvvPVnYdz+epb7Yzd+NhXri+A52jA2u+aBERAS5hHyKRi3E0t5hvfj7CnPWp9puoerlbualbE8b1a0aLMD+TK7x4GVm5DH/k1Wrb5r326Hm/TofGAXw/4TL+ve4Qry/ew9Yjedzw7hpG9mzKpD+1ItRPl9FERGraRQWiq6666py/vS9btuyiCxLHkFdUzrLkdLYdySc5PZ/C0kpshkEDTzeiAnxo3MiHuNCGNP/1Hl5nu6xVUWljX2YBq/acIGF3OutTsu2XxUJ8vRgTH8PtvZuaMlfmYs/onO15u3bvZngN1ebuZmXsZbEM6xjJtB93880vR/l8QyrfJR3l3svjuPeKOJe4lCgiUlcu6jvq6flDp5WXl5OUlMT27dvPuOmr1B+3jhrNkQLIC+tEYaMWGFaPP37SryL8vQkP8CbAxwNPNwtFZZVkF5Zx4EQhZRVVdzLvExfEzd2jubZzZI3dQmPnzu1cNeTMOHKuYHOxZ3TO9rwtY4ecZ7XnL8zfmzdu7cLIntH844ddbDmSx1sJe5mz/hB/HdCSkT2b4umuvYsuxqVe4hQR53JRgeiNN96o9vizzz5LQUHBJRUk5iirsJHs3pz8dj2w/XoGJ7ihJ9GNGnBk7bf88+lHsVrhZEkFx3JLSM0u4kBmAfszCzlRUEpafglp+SXVvraPhxs9Y4Po3yqUP7ULJzqoQY3XX26j2pByIZeqHFnvuGC+HX8ZP2xL45VFuzmYVcSU73bw0eoUHh3UmmEdI7Fq4vUFqalLnCLiHGr0nPsdd9xBr169ePXV6r/JiGM6klPEPZ9sIjeyBxjQLLgBPZsFERngfWrTw/nJDGwXftbn5xWVc+BEAVkFZeQUlVFhO3Vpzd/bg+ahvjRp5KMf1jXAYrEwrFMkg9qH88XGw7y1dC+Hsop48PNf+GDVAZ4Y0oa+LULMLlNEpF6q0UCUmJiIt7d3Tb6k1LLtR/MYO2sjmSdLsZYXM7hrLC3DfC9ohVdAAw+6Otm9uM52CQ5qdq7QxfBws3Jnnxhu6tqYj1an8P9W7mfrkTxu/3A9V7YKpdzT38TqRETqp4sKRDfddFOVrw3D4Pjx42zatIlnnnmmRgqT2rf+QBZjZ22kqKySNhF+FCbMptWQ58wuyyGc7RIc1M5coYvR0Mudvw5oye29m/KvZfuYs/4QK/dkYmlzM+tTsugRE6T9i0REztNFzcYMCAio8ggKCqJ///788MMPTJ06taZrlFqw/Wge93yyiaKySi5rEcyX98fjXl5odllyEUJ8vXh2eHuWPHwlV7QKxbC6s+5ANnM3HSa7sOyPX0BERC7uDNHMmTNrug6pQ/szCxj98QZOllbQOzaIj8b0xNujZlZ7iXmahTTkk7E96XXbJE62HETmyVI+35DKFa1C6RDlX+82uhQRqUuXNIdo8+bN7Nq1C4D27dvTtWvXGilKak9ecTn3fLKJ7MIyOjT258MxPRwqDJ1rKbTZc3cuxbnmJNXkEm+LxYJv7n6G945h8c40DmcXs2x3BoeyChnQ9uwT40VEXN1FBaKMjAxGjhzJihUrCAwMBCA3N5errrqKL774gtDQ0JqsUWqIzWYwaW4SKScKiQrwZtbYXvh5n/9eQ3XhXEuhHWXuzsU415yk2lji7evlzo1dGvNLai5r9p9gf2Yh6fmp+DWMqPH3EhFxBhcViB588EFOnjzJjh07aNu2LQA7d+5kzJgx/PWvf+Xzzz+v0SKlZry9bC8JuzPwdLfy/+7sQYjupO4QauvskcVioVtMI6KDGvDj9uPkFJVT0OI63v9pP/deHqdLaCIiv3FRgWjhwoUsXbrUHoYA2rVrx/Tp0xk0aFCNFSc1Z+PBbN5O2AvAP27sSMcmASZXVDccefn8abV99ijUz4uRPZuybHcGyekn+ccPu9mQksNrN3cmoIFjnSEUETHLRQUim82Gh8eZ30g9PDyw2WzVPEPMdLKknIfnJmEz4M/dm/Dn7k3MLqnO1Ifl83XB093K4PbhnNi0gJPN+rN0VzrD3lnFu6O60alJoNnliYiY7qKW3V999dU89NBDHDt2zH7s6NGjPPzwwwwYMKDGipOa8ey8nRzJKaZJIx+mXtfO7HLEJBaLBf+sXXz9l740DWrAkZxi/vxeIv9OPIhx+o67IiIu6qLOEP3rX/9i+PDhNGvWjOjoaAAOHz5Mhw4dmD17do0WKJdmeXIG//35CFYLvHFrF4eZRF0Xd4yX6nVoHMD3D/bj8f9sYdGOdJ75bgeJB7L4+w0dCWroaXZ5IiKmuKhAFB0dzc8//8zSpUvZvXs3AG3btmXgwIE1WpxcmpLySqZ+twOAsZfF0rNZkMkV/U9d3jFezhTg48GMO7rz0eoU/vnjbn7YlsaGlGxevLEjg9trJZqIuJ4LumS2bNky2rVrR35+PhaLhT/96U88+OCDPPjgg/Ts2ZP27duzatWq2qpVLtD05ftIzS4iMsCbh//UyuxyxMFYLBbuuTyOb/5yGa3CfTlRUMb//Xsz93yykcPZRWaXJyJSpy4oEL355pvce++9+PufefPIgIAA/u///o/XX3+9xoqTi7c/s4AZK/cDMPW6dvh61eh9fMWJdGwSwLwJ/Xigf3PcrRaW7spg4OsreXnhbvJLys0uT0SkTlzQT8ktW7bw0ksvnbV90KBBvPpq9St6pG699ONuyisNrmodesmXQOpql2U509n+7mv6793bw43JQ9pwU9fGTPl1TtG7K/bz+YZUHry6JXf0icHT/aLWYIiI1AsXFIjS09OrXW5vfzF3dzIzMy+5KLk0mw/lsHhnOlYLPDWs7SVvwFfXuyzL/5zt7762/t5bhvvx2b29WbIznZcW7mZ/ZiHPz9/JrLUHeXRwa67tGInVqg0dRcT5XFAgaty4Mdu3b6dFixbVtm/dupXIyMgaKUwujmEYvLTw1ET3P3dvQoswv1p9P509cj4Wi4VB7SO4uk0YX246whtL95CaXcRfP/+FD346wJND29C3RYjZZYqI1KgLCkTXXHMNzzzzDEOGDMHb27tKW3FxMVOnTuXaa6+t0QLlwqxIzmRDSjae7lYmDqz9idQ6e2SOutiB293Nyu29m3JD1yg+WpXCjJX72XY0j9s/XE//1qE8MbQNbSLOnE8oIlIfXVAgevrpp/n6669p1aoVEyZMoHXr1gDs3r2b6dOnU1lZyVNPPVUrhcofMwyDN5fuAWBMfAxRgT5n9HHWu8m7mrrcgbuBpzsPDmjJbb2b8k7CXuasT2VFciYr92Ry/5XNmfSnVni4aX6RiNRvFxSIwsPDWbt2LQ888ABPPvmkfXdbi8XC4MGDmT59OuHh4bVSqPyxDSnZbDmSh5e7lf+7snm1fZz1bvJS+0J8vXju+g6MvSyWVxYls2Dbcd5bsZ+1+7P4121diQ5qYHaJIiIX7YLXYsfExPDDDz+Qk5PDvn37MAyDli1b0qhRo9qoTy7AB6sOADCiexPdyV5qTbOQhkwf1Y1rtx1n8n+3suVwLje+u4aZd/VymZsGi4jzuejNaRo1akTPnj1rsha5BPsyTrJ0VwYWC9zTL9bscoD6cad5uXhDO0bSKTqQ+z7dxI5j+Yx8P5EZd3bn8pahZpcmInLBtFufk/hwVQoAf2obTlyor8nVnKI7zTu/xoE+fHFfH+6fvZk1+7IY98kmPr27F33igs0uTUTkgmgmpBPIKyrn61+OAnDfFXEmVyOuxs/bg5l39eJP7cIpq7Bx7yeb2HEsz+yyREQuiAKRE5i35ShlFTbaRPjRPUZzuaTuebpbeee2rvSKDeJkaQVjPt7I0dxis8sSETlvCkRO4MtNRwC4pUf0Je9KLXKxvD3c+HBMD9pE+HGioJS/zN5MaUWl2WWJiJwXBaJ6btfxfLYdzcPDzcINXRubXY64OH9vDz4Y3YPABh5sOZLHc9/vNLskEZHzoknV9dTpDRazouIhrBMeWfsZMWIGoFtmiLmigxrw5q1dGDtrI5+tT6VHTCNu6tbE7LJERM5JgaieysjKZdjDr/DR6hQor2Tglf2IHTEY0C0z5OzOthVCTYfo/q3D+OvVLXkrYS9Tv9tBn7jgandOFxFxFApE9dih7EKKyytp4OlGzG92Cdb+P3I2Z9sK4Vwh+ly3ezmwbw9xLaq/Z15ocCO69L2PpMO5PP6frfx7XC/NcRMRh6VAVI/tzygEoGWYL1br/37QaP8fqUnnut3LC2OHnPPmvh/e0plr3lrF6n0nmL0+lTv7xNRmqSIiF02TquspAwsHThQA0NxBNmIU+b3mob5MHtIGgH8s2MWhrEKTKxIRqZ7OENVTJb6RlJTb8Paw0lhzM8QBnb50awDeza+l2K8xg6Z+TuecVXw55xOzyxMRqUKBqJ4qCmgGQFxI1ctlIo7it5du84vLmb3+EKW+kezLiza5MhGRMykQ1UOGYVAYcOoGrs1DG5pcjTiD2p6I7+/jwRUtQ0nYnUF2ZC/2pp+kZbjfJb6qiEjNUSCqh7YeyaPS0xcPNwtNf7O6TORi1cVE/PZR/uzPLOBgVhGPfLWFrx/oi7ubpjGKiGNQIHJw1f3mnhPRAyK60yy4oX6gSL1hsVgY0DacmSt2s/VIHu+t2M+DA1qaXZaICKBA5PCq+8197sbDkF9Cs2BdLpP6xdfLneCja8iMuZq3l+1lQNtw2kX5m12WiIiW3dc3JeWVpOeXABAdpNVlUv80zNnLoHbhlFcaTPoyibIKm9kliYiYG4h++uknrrvuOqKiorBYLHz77bdV2g3DYMqUKURGRuLj48PAgQPZu3dvlT7Z2dmMGjUKf39/AgMDGTduHAUFBVX6bN26lcsvvxxvb2+io6N5+eWXa3toteZITjEGUJmXjp+3h9nliFwwC/DijR1p1MCD3WkneWfZ3j98johIbTM1EBUWFtK5c2emT59ebfvLL7/M22+/zYwZM1i/fj0NGzZk8ODBlJSU2PuMGjWKHTt2sGTJEubPn89PP/3EfffdZ2/Pz89n0KBBxMTEsHnzZl555RWeffZZ3n///VofX21IzS4CoPL4bpMrEbl4oX5e/P2GjgC8u2I/Ww7nmluQiLg8U+cQDR06lKFDh1bbZhgGb775Jk8//TTXX389AJ9++inh4eF8++23jBw5kl27drFw4UI2btxIjx49AHjnnXe45pprePXVV4mKimLOnDmUlZXx8ccf4+npSfv27UlKSuL111+vEpzqi8O/BqKKtGSTKxG5NMM6RfLj9kjmbz3Og5//wvy/9sNfZz1FxCQOO4coJSWFtLQ0Bg4caD8WEBBA7969SUxMBCAxMZHAwEB7GAIYOHAgVquV9evX2/tcccUVeHp62vsMHjyY5ORkcnJyqn3v0tJS8vPzqzwcQX5xObnF5VgsUJGuywxS/714Q0eaNPIhNbuIx7/aimEYZpckIi7KYQNRWloaAOHh4VWOh4eH29vS0tIICwur0u7u7k5QUFCVPtW9xm/f4/emTZtGQECA/REd7Rg766bmnDo7FOHvDeUlf9BbxPEFNPBg+u3d8HCzsHBHGh+vOWh2SSLiohw2EJnpySefJC8vz/44fPiw2SUB/7tcFq3NGMWJdI4O5Klr2gLwjx92sSI5w+SKRMQVOWwgioiIACA9Pb3K8fT0dHtbREQEGRlVv3lWVFSQnZ1dpU91r/Hb9/g9Ly8v/P39qzzMZhgGR3OLAYhupOX24lzG9G3GiG5NqLQZTPjsF3anOcZlahFxHQ4biGJjY4mIiCAhIcF+LD8/n/Xr1xMfHw9AfHw8ubm5bN682d5n2bJl2Gw2evfube/z008/UV5ebu+zZMkSWrduTaNGjepoNJfuZEkFhaWVWC0Q7u9tdjkiNcpisTDtpo70iQuioLSCu2dutP8CICJSF0wNRAUFBSQlJZGUlAScmkidlJREamoqFouFiRMn8ve//5158+axbds2Ro8eTVRUFDfccAMAbdu2ZciQIdx7771s2LCBNWvWMGHCBEaOHElUVBQAt99+O56enowbN44dO3Ywd+5c3nrrLSZNmmTSqC/OsbxTPxxC/bzw0O06xAl5uluZcUd34kIbciyvhNs/WEdanubKiUjdMPUn66ZNm+jatStdu3YFYNKkSXTt2pUpU6YA8Pjjj/Pggw9y33330bNnTwoKCli4cCHe3v87QzJnzhzatGnDgAEDuOaaa+jXr1+VPYYCAgJYvHgxKSkpdO/enUceeYQpU6bUuyX3x3NP/WCIDNDlMnFegQ08mXNPb6KDfDiUVcTtH64jI1+hSERqn6n7EPXv3/+cy2wtFgvPP/88zz///Fn7BAUF8dlnn53zfTp16sSqVasuuk5HcPoMUVSALpeJc4sM8OGze/ow8v11HMgs5Kb31vLvcb2JDdG9+0Sk9ujaSz1QWlFJVkEZAJGBOkMkzi86qAFf3NeHZsENOJJTzJ/fW8vWI7lmlyUiTkyBqB5IyyvBAPy93fH1MvWknkidiQ5qwH8e6EuHxv5kFZZx2/vrWLU30+yyRMRJKRDVA8fzNH9IXFOIrxef39uHy1oEU1hWyd2zNvJd0lGzyxIRJ6RAVA/YA1Gg5g+J6/Hz9uDju3oyrFMk5ZUGD32RxMerU8wuS0ScjAKRo7NY7EuPo3SGSFyUl7sbb4/syuj4GACen7+Tf/64W/c+E5Eao0Dk4Kz+4ZRV2nC3Wgj29fzjJ4g4KTerheeGt+exwa0BmLFyP1Pn7cBmUygSkUunQOTg3IJO3Vg21M8Lq8VicjUi5rJYLIy/qgXTbuqIxQKfJh7iqW+3KRSJyCVTIHJw1l8DUbif5g+JnHZbr6a8+ufOWC3w+YbDPPPddl0+E5FLokDk4NyCmwIQ5u9lciUijmVE9ya8cWsXLBaYsz6VVxcnm12SiNRjCkQOzGYYuAU1ASDMT4FI5Peu79KYv9/QAYDpy/fz4aoDJlckIvWVApEDyyksw+LuhYebhUYNNaFapDqjescQnbsFgL/P30nvWx7kqiHDuWrIcG4dNdrk6kSkvlAgcmAZJ0sBCPXVhGqRc3E7uI4Ojf3BYiG75VDi73uR4Y+8SkZWrtmliUg9oftAOLCM/FOBKMxfE6pFbh01+qwBZ/fu3Tz6cBh5ReUczinm+63HuL1X07otUETqNQUiB5Z+8tSGjJo/JAIZWbkMf+TVatu2jB2Cm9XCNR0j+WLjYfKKy1m8Mx2tOxOR86VLZg7KZhhk/nrJLFxniETOi7eHG8M6RuJmtZByopC80M5mlyQi9YTOEDmonMIyKmwGRnkpgQ08zC5HpMbs3Lmdq4YMr7btwL49xLVoVW3brt27qf5ZVYX6eXFlq1CW7c4gJ6oXGw9m07NZ0CVULCKuQIHIQZ0+O1SZcwSrpYPJ1YjUnHIbZ7309cLYIee8LHa+OkT5czSnmOT0kzz42S8s+Gs/gn116VlEzk6XzBzUiYIyAGw5R02uRKT+sVgsXN0mDI+SHNLyS3j4yy26vYeInJMCkYPKLDh9hkiBSORieLpbCTu4FG8PKz/tyeS9lfvNLklEHJgCkYM68WsgsuUeM7kSkfrLsySb568/dcn59SV72Hwox+SKRMRRKRA5oKKyCorKKgGoVCASuSQ3d2/C8M5RVNoM/vr5L+QVl5tdkog4IAUiB3R6/lCAjwdUlJlcjUj9ZrFYePHGDkQH+XA0t5i/fb0Nw9B8IhGpSoHIAZ2+XBaqVTEiNcLP24N3buuGu9XCgm3HmbvxsNkliYiDUSByQCd+XXIf4qsbuorUlC7RgTw6uDUAz36/gzKvQHMLEhGHokDkgE5fMgvRLTtEatR9l8dxecsQSsptZDQbSEWlzeySRMRBKBA5mEqbQXbhr4FIl8xEapTVauG1WzoT4utJuU8wq/adMLskEXEQCkQOJqeojErDwNPNir+3NhIXqWlhft68dksXALYeyWN/ZoG5BYmIQ1AgcjCnJ1QH+3pisVhMrkbEOV3ZKpSAjC0ALNmZTn6JluKLuDoFIgdjnz+ky2UitarR8Q2E+3tRWmFjwdbjmk8k4uIUiBxM1m/OEIlI7bEYNq7pEIm3h5WMk6UsT87U/kQiLkyTVBzM6QnVwQ0ViEQu1c6d27lqyPBq23bt3s1wHw+GtI/gu6Rj7DyeT7i/F52aBNZtkSLiEBSIHEh5pY38kgoAghvqkpnIpSq3wfBHXq22bcvYIQDEBDekb/Ng1uzPYsWezFM7xIuIy9ElMwdy+uyQj4cbPp5uJlcj4jq6xzSibYQfhgE/bEujzLuR2SWJSB1TIHIgWbpcJmIKi8XC1W3DiAr0pqzSRlrcUI7lFptdlojUIQUiB5L96wqzIE2oFqlz7lYr13aKolEDDyo9/bjzo/X2s7Yi4vwUiBxIVuGvK8x0hkjEFD4ebtzQtTFuZSfZn1nImI83kFekPYpEXIECkQM5/dtokAKRiGn8vT2I2L+AoIaebDuax6iP1pGjM0UiTk+ByEGUVWiFmYij8CzN47N7exPc0JPtR/O57YN1ZJ4sNbssEalFCkQOIrtIK8xEHEmbCH++uK8PoX5e7E47yU3vrdF9z0ScmAKRgzg9oVo7VIs4jpbhfnz1f/HEBDfgcHYxI95by/oDWWaXJSK1QIHIQWiHahHH1CykIf99oC+dowPJLSpn1Ifr+TTxoG7zIeJkFIgcxOkVZppQLeJ4Qny9+OLePlzXOYoKm8GU73bwyJdbKCitMLs0EakhCkQO4n+bMmpCtYgj8vF04+2RXXjqmrZYLfD1L0cZ9vYqkg7nml2aiNQABSIHYLO6c/LXFWbalFHEcVksFu69Io4v7osnKsCbQ1lFjHhvLdN+3EVxWaXZ5YnIJVAgcgDlXqfum9TA0w0fD60wE3F0vWKD+PGhK7i2UySVNoP/t/IAQ976iTX7TphdmohcJAUiB1DmcyoQaf6QSP0R0MCDf93ejQ9G9yDC/9TZolEfrufRr7ZoI0eResjd7AIEyr2DAK0wE3EUO3du56ohw884HhYcyNw5n1Y59qd24fSJC+KVRcn8e90h/rP5CEt3pfPY4NaM7NkUN6ulrsoWkUugQOQAyrx1hkjEkZTbYPgjr55xfN5rj1bb38/bg+ev78D1XRrz1Dfb2J12kqe+2c4XGw7z3PXt6da0UW2XLCKXSJfMHED5r4FIK8xE6rfuMY2Y/2A/pl7XDj8vd7YdzeOmd9fy+H+2cKJAt/4QcWQKRCYrLK2gwtMP0AozEWfg7mZl7GWxLHu0P3/u3gSALzcd4apXVzBrTQoVlTaTKxSR6igQmWxvxql7I2mFmYhzCfXz4tWbO/PfB/rSobE/J0sqePb7nfx5RiL7Mk6aXZ6I/I4Ckcn2pJ/6xqj5QyLOqXtMI74b34+/39ABP293kg7ncs3bq5mxcj+VNt3+Q8RRKBCZbN+vZ4i0wkzEeblZLdzRJ4bFD19B/9ahlFXY+OePuxnx3lr79wARMZcCkclOnyHShGoR5xcZ4MPMu3ry8ohO+HmdOlt07Tur+OaXI2aXJuLytOzeZHvTT/12qEtmIo7vbPsTQfV7FFXHYrFwS89oLm8VwuP/2cqqvSd4eO4WNh/K4Zlr2+HlrrmEImZQIDJRQWkFR3OLAQjWCjMRh3e2/Yng7HsUnU1kgA+zxvbirYS9vJ2wl9nrUtl2NJ93R3WjcaBPTZQrIhdAgchEGfklNGnkw/H0E3hrhZlIvXaus0cH9u0hrkWratvCggOZ+cyrTJybxJbDuVz79iqmj+pG3+YhtVmuiPyOApGJ4kJ9WT35avoPvQGGdjG7HBG5BOc6e/TC2CHnPLN0VZsw5j/Yj7/M+ZltR/MY8/EG/nlTJ0b8uo+RiNQ+Tap2ABZDG7WJuLrooAZ8dX8813WOorzS4JGvtvDGkj0Yhpbmi9QFBSIREQfh7eHGW7d24S/9mwPwVsJeHvlyC2UV+qVJpLY59CWzZ599lueee67KsdatW7N7924ASkpKeOSRR/jiiy8oLS1l8ODBvPvuu4SHh9v7p6am8sADD7B8+XJ8fX0ZM2YM06ZNw93doYcuIi7ibHOPYpt2IzWkF1//cpRjecV8MLoHft4edV7fraNGk5GVW23b+a6sE6kPHD4VtG/fnqVLl9q//m2Qefjhh1mwYAFfffUVAQEBTJgwgZtuuok1a9YAUFlZybBhw4iIiGDt2rUcP36c0aNH4+HhwT/+8Y86H4uIyO+dbe7RvNce5eNHxzN+zs+sO5DNbR+s45OxvQj2rds9yzKycmtsZZ2II3P4S2bu7u5ERETYHyEhp1Ze5OXl8dFHH/H6669z9dVX0717d2bOnMnatWtZt24dAIsXL2bnzp3Mnj2bLl26MHToUF544QWmT59OWVnZWd+ztLSU/Pz8Kg8Rkbp2ZatQvrivD8ENPdl+NJ+b/1+ifasOEalZDh+I9u7dS1RUFHFxcYwaNYrU1FQANm/eTHl5OQMHDrT3bdOmDU2bNiUxMRGAxMREOnbsWOUS2uDBg8nPz2fHjh1nfc9p06YREBBgf0RHR9fS6EREzq1D4wC+uj+exoE+HMgs5M+63YdIrXDoQNS7d29mzZrFwoULee+990hJSeHyyy/n5MmTpKWl4enpSWBgYJXnhIeHk5aWBkBaWlqVMHS6/XTb2Tz55JPk5eXZH4cPH67ZgYmIXIC4UF/+80A8zUMbcjyvhFv+XyLbjuSZXZaIU3HoOURDhw61/7lTp0707t2bmJgYvvzyS3x8am8nVy8vL7y8dG8xEXEckQE+fHV/X+6auYGtR/K47YN1fDC6B/HNg80uTcQpOPQZot8LDAykVatW7Nu3j4iICMrKysjNza3SJz09nYiICAAiIiJIT08/o/10m4hIfRLU0JPP7u1D3+bBFJRWMGbmBhbvOPvZbhE5f/UqEBUUFLB//34iIyPp3r07Hh4eJCQk2NuTk5NJTU0lPj4egPj4eLZt20ZGRoa9z5IlS/D396ddu3Z1Xr+IyKXy9XLn47t6MqhdOGUVNh6Y8zNf/3zE7LJE6j2HDkSPPvooK1eu5ODBg6xdu5Ybb7wRNzc3brvtNgICAhg3bhyTJk1i+fLlbN68mbFjxxIfH0+fPn0AGDRoEO3atePOO+9ky5YtLFq0iKeffprx48frkpiI1FveHm68O6obI7o1odJmMOnLLXyy9qDZZYnUaw49h+jIkSPcdtttZGVlERoaSr9+/Vi3bh2hoaEAvPHGG1itVkaMGFFlY8bT3NzcmD9/Pg888ADx8fE0bNiQMWPG8Pzzz5s1JBGRGuHuZuWVP3fCz9udWWsPMnXeDk6WlDP+qhZYLBazyxOpdxw6EH3xxRfnbPf29mb69OlMnz79rH1iYmL44Ycfaro0ERHT/Hb3aAMIjOhObkQPXl28h9n/mUfi9EkKRSIXyKEDkYiIqzrbLT0Adu3ezeQP5lc59ktqDj/tPUGafxue+O82/nFTR9ysCkUi50uBSETEAZ3tlh4AW8YOOeNY16aN8HS3snRnGnM3HaagtII3bu2Cp7tDTxUVcRj6P0VExEm0jwog7OBSPN2sLNh2nHs/3URxWaXZZYnUCwpEIiJOpGFeCh/d1QMfDzdW7snkzo/Wk1dcbnZZIg5PgUhExMlc3jKU2ff0wt/bnU2Hcrjt/XWcKCg1uywRh6ZAJCLihLrHBPHFffGE+Hqy83g+t8xI5GhusdlliTgsBSIRESfVLsqfr+7vS+NAHw6cKOTm99ayL+Ok2WWJOCStMhMRcSLVLde3ejTEo/kwjtGIEe8l8uGYHvRsFmRShSKOSYFIRMSJnG25fnFZJZ8sXEseEYz6cD1v3dqFoR0jTahQxDHpkpmIiAvw8XQjYt8C+01h//LZz3y8OsXsskQchgKRiIiLsBoVvHdHd+7sE4NhwPPzd/KPH3ZhsxlmlyZiOgUiEREX4ma18Pz17Zk8pA0A7/90gIfmJlFaoQ0cxbUpEImIuBiLxcID/Zvzxq2d8XCz8P2WY4z+aAO5RWVmlyZiGgUiEREXdWPXJswa2wtfL3fWp2Rz07trOXii0OyyREyhQCQi4sIuaxHCfx6It+9VdOO7a9h4MNvsskTqnJbdi4i4uKmPTMCaV4pn3BByCOPmd1cTmroC39x97Nq9m+F//BIi9Z4CkYiIi6hu00aAXbt3M/mD+ZRX2li0I439mYVkNhtAXOzNbHlhlAmVitQ9BSIRERdxtk0bt4wdAoCHm5VhHSNZsz+LzYdyWJ+Sjfdlo6mw2XC3aoaFODf9CxcRETuLxUK/FiEMaBOG1QKesT355uejFJdpWb44NwUiERE5Q4fGAVzfpTFGWRHH8kqYu+kwOYVali/OS4FIRESq1TSoAYWL3sDf25284nLmbjrMkZwis8sSqRUKRCIicla2vDRu7RlNhL83pRU2vvnlKDuP5ZtdlkiNUyASEZFzauDpzohujWkV5ovNgCW70lm99wQGFrNLE6kxCkQiIvKH3N2sDOkQQa9mQQBsTs0hPXYw+SXlJlcmUjMUiERE5LxYLBbimwczpH0E7lYLxQEx3DB9DQcyC8wuTeSSKRCJiMgFaR3hx83dm+BWVsCBzEKun76GlXsyzS5L5JIoEImIyAUL8/em8Z6v6R7TiJMlFYyduYEPVx3AMAyzSxO5KApEIiJyUdwqivns3t7c0qMJNgP+vmAXj361lZJybeIo9Y8CkYiIXDQvdzdeGtGJqde1w2qB//58hJveXUtqlvYrkvpF9zITEZGL8vubxYb5NiYjZgA7j8NVLy3i/bF9GdA23MQKRc6fApGIiFyU6m4We7KknB+2pZGWD+M+2cSEq1rw8J9a4WbVnkXi2HTJTEREaoyftwd/7t4E/8xtAPxr+T7GfLyBrIJSkysTOTcFIhERqVFuVgvBR9fy1sgu+Hi4sXrfCa59ZzU/p+aYXZrIWSkQiYhIrbi+S2O+m3AZcSENOZ5Xws0zEnlr6V4qKm1mlyZyBgUiERGpNa3C/fhuwmVc1zmKSpvBG0v3MGJGIiknCs0uTaQKBSIREalVft4evHNbV94a2QU/b3e2HM7lmrdWMXvdIW3kKA5DgUhEROrE9V0as2jiFfRtHkxxeSVPf7ud2z9Yz37dC00cgAKRiIjUmahAH2aP680z17bD28NK4oEshr65ipcX7uZkSbnZ5YkL0z5EIiJS436/aeNpYcGBzJ3zKeP6xTKoXThTvtvO8uRM3l2xny83HeahAS25pWc0Xu5uJlQtrkyBSEREalx1mzYCzHvtUfufo4Ma8PFdPVm6K4NpP+ziwIlCnvluB++t2M8DV7VgRLfGNPDUjympG7pkJiIiprFYLPypXTiLHr6C569vT7i/F8fySnjm2+30/kcCz3+/kwOaYyR1QNFbRERM5+FmZXR8M27pEc3cjYf5eE0Kh7KK+HhNCh+vSeHyliHc0iOaAW3DdNZIaoX+VYmISJ0529wi+N/8ojF9m3Fnnxh+2pvJvxMPsSw5g1V7T7Bq7wm8PawMaBPOsE6RXNU6DB9PzTWSmqFAJCIideZsc4ug6vwiq9VC/9Zh9G8dxuHsIuZuPMz3W49xKKuIBduOs2DbcXw83LiyVSiDO4RzdetwAhp41NUwxAkpEImIiEP4o7NHK2Z/wo5j+czfepz5W49xJKeYhTvSWLgjDXerhT5xwQxqH86gdhFEBHjXcfVS3ykQiYiIQ/ijs0cWi4UOjQPo0DiAyUNas+NYPot2pLF4RzrJ6SdZve8Eq/edYMp3O+jcJIBB7SMY3D6cFmF+dTwSqY8UiERExOGd7ezRgX17iGvRiiae/hQFNKMwIJbShuFsOZLHliN5vLIombjQhgxqdyocdW4SiNVqMWEE4ugUiERExOGd7ezRC2OHnHG8sLSCAycKSdywiYpGMRzILGTGyv3MWLmfcH8v/tQunCHtI+kdF4SHm3afkVMUiERExKk09HKnY+MAFsx/hTYdulLk35SigFiK/KNJz4fZ61KZvS6VAB8PBrQNY3D7CK5oGaoVay5OgUhERJxSuQ1ueHia/esKm43D2cXszyxg16F08oCvfz7K1z8fta9YG9gunCtbhRLq52Ve4WIKBSIREXEJ7lYrsSENiQ1pSOEPr/LyezNZuD2NRTvSOJr7vxVrAB0bB9C/dSj9W4fRJToQtz+Yd3TrqNFkZOVW23Z6fyVxbApEIiLiciwY9IoNoldsEM9c25Ydx/JZvCONFXsy2Xokj21HTz3eWbaPwAYeXN4ylMtbhtCvRQhRgT5nvF5GVu557a8kjkuBSEREXM659jzqFhrO7ROnsiI5g5/2ZJJbVM73W47x/ZZjAMSFNqRfixAuaxFCn7hgAny0IaQzUCASERGXc649j/45bgg5mekABGGhYcMwivyakusRhCWoKQcyCzmQWciniYfAsOFVlEmhRyxHcoqI8PfGXSvX6iUFIhERkd841xL/x99fwJHcYlKzizicXUROUTmlDcNxbz+I//58FKsFwvy8iQw49YgI8MYwYQxy4RSIREREzpOXhxvNQ31pHuoLwMmScg5nF7Pgh/kEtu5DUVklafklpOWX8MvhU8+xdriLW/5fIm0i/Ggd4UdscEMaN/IhMsAHT3fXOpvkyJPPFYhEREQukp+3B+2iPPjvmk+5Z9xt5JdUcDyvmOO5JRzLKyarsAybuxcbUrLZkJJd5bkWC0T4e9OkkQ+NA31o0qgBjRv5EBV46uvGgT5OtzeSI08+VyASERGpARaLhQAfDwJ8PGgT4Q+c2vvomxkv8egzL5CcdpLk9JMczi7iSE4xpRU2jueVcDyvhI3kVPuaQQ09iQr0JrpRA2JDGhIX6kvz0FP/1WTumqVAJCIiUkvcrVb2b17JO3/7S5XjkYDN3Zu9x7K5YdJL5JeUk19cwcmSck6WVHAiNw+Lhw/ZhWVkF5ax/Wj+Ga8d4utJXIgvzcMaEhfiS1xoQ2KCGxId5IOXu3OdWaoLLhWIpk+fziuvvEJaWhqdO3fmnXfeoVevXmaXJSIiTuxcK9peGDuEVuF+1R5//P0F5JecCkl5xeXkFJWTU1TG8YwTVHo05ERBGScKstlw8MxLcVEBPjQLaUBMcEOaBTcgulEDwgO8Cff3JtTXq07nLhWUVpCRX0J6fikFgS3YfCiH4vJKKm0GNptBpXHqv1mNL6uzmqrjMoFo7ty5TJo0iRkzZtC7d2/efPNNBg8eTHJyMmFhYWaXJyIiUoWXhxuhHm5n3EZk3mtvMu/b/5JyovDXLQAK2J9ZyIEThaRmFVJYVsnR3GKO5hazZl9Wta8d4utJmJ834f5eBDX0IsDHA38fd/slP39vD/x9PPByt+LuZsHDzYq71YLFYqGswkZZhY3SikrKKmzkFZeTXVRGTmEZ2YXlZBWWkp5fQkb+qf8WllX+742bDSBz34lqa3ILjK2xv7uL4TKB6PXXX+fee+9l7NixAMyYMYMFCxbw8ccf88QTT5hcnYiIyPnZuXM7w28YUW1bwb49xLTpTLmXP+WeAVR4+VPuFUBBpRse/iFUuDcAq9uvZ5fK2Hm8bmr29XInzN+LYwf2ENOiDT6ebrhbLVitFtysFtwsFnYt/wm4pW4KqoZLBKKysjI2b97Mk08+aT9mtVoZOHAgiYmJZ/QvLS2ltLTU/nVeXh4A+flnXsOtCRUV5ZQUFlTbZths1bad7XhttNXn96rPtdfle9Xn2uvyvepz7c76XvW59ot9r7IKG4Puf7batpf/chND75lc7fHH3/0awzAorqikqLSSwtIKCssqWPrfTwkOb4zNzQubmxeVbh7Y3LwwrB5Y3D0IDGxEhc2g3GbDMMDTzYqnu5WcrBMYFRVYbKW4VZZgrSjFraKUvIyjhPr7YK0owr28CLfyYqxGOeVA9p49jHn7q2prP5S+tcZ/zp5+PcM4j92gDBdw9OhRAzDWrl1b5fhjjz1m9OrV64z+U6dONQA99NBDDz300MMJHocPH/7DrOASZ4gu1JNPPsmkSZPsX9tsNrKzswkODsZiOfcdj82Sn59PdHQ0hw8fxt/f3+xy6ozGrXG7AlcctyuOGTTumh63YRicPHmSqKioP+zrEoEoJCQENzc30tPTqxxPT08nIiLijP5eXl54eVWdxBYYGFibJdYYf39/l/qf6DSN27Vo3K7DFccMGndNCggIOK9+LrFnuKenJ927dychIcF+zGazkZCQQHx8vImViYiIiCNwiTNEAJMmTWLMmDH06NGDXr168eabb1JYWGhfdSYiIiKuy2UC0a233kpmZiZTpkwhLS2NLl26sHDhQsLDw80urUZ4eXkxderUMy71OTuNW+N2Ba44blccM2jcZo7bYhjnsxZNRERExHm5xBwiERERkXNRIBIRERGXp0AkIiIiLk+BSERERFyeAlE9M23aNHr27Imfnx9hYWHccMMNJCcnV+lTUlLC+PHjCQ4OxtfXlxEjRpyxKWV9895779GpUyf7pl3x8fH8+OOP9nZnHPPv/fOf/8RisTBx4kT7MWcc97PPPovFYqnyaNOmjb3dGcd82tGjR7njjjsIDg7Gx8eHjh07smnTJnu7YRhMmTKFyMhIfHx8GDhwIHv37jWx4kvXrFmzMz5vi8XC+PHjAef8vCsrK3nmmWeIjY3Fx8eH5s2b88ILL1S535YzftYAJ0+eZOLEicTExODj40Pfvn3ZuHGjvd3UcV/6ncKkLg0ePNiYOXOmsX37diMpKcm45pprjKZNmxoFBQX2Pvfff78RHR1tJCQkGJs2bTL69Olj9O3b18SqL928efOMBQsWGHv27DGSk5ONv/3tb4aHh4exfft2wzCcc8y/tWHDBqNZs2ZGp06djIceesh+3BnHPXXqVKN9+/bG8ePH7Y/MzEx7uzOO2TAMIzs724iJiTHuuusuY/369caBAweMRYsWGfv27bP3+ec//2kEBAQY3377rbFlyxZj+PDhRmxsrFFcXGxi5ZcmIyOjyme9ZMkSAzCWL19uGIZzft4vvviiERwcbMyfP99ISUkxvvrqK8PX19d466237H2c8bM2DMO45ZZbjHbt2hkrV6409u7da0ydOtXw9/c3jhw5YhiGueNWIKrnMjIyDMBYuXKlYRiGkZuba3h4eBhfffWVvc+uXbsMwEhMTDSrzFrRqFEj48MPP3T6MZ88edJo2bKlsWTJEuPKK6+0ByJnHffUqVONzp07V9vmrGM2DMOYPHmy0a9fv7O222w2IyIiwnjllVfsx3Jzcw0vLy/j888/r4sS68RDDz1kNG/e3LDZbE77eQ8bNsy4++67qxy76aabjFGjRhmG4byfdVFRkeHm5mbMnz+/yvFu3boZTz31lOnj1iWzei4vLw+AoKAgADZv3kx5eTkDBw6092nTpg1NmzYlMTHRlBprWmVlJV988QWFhYXEx8c7/ZjHjx/PsGHDqowPnPuz3rt3L1FRUcTFxTFq1ChSU1MB5x7zvHnz6NGjBzfffDNhYWF07dqVDz74wN6ekpJCWlpalbEHBATQu3fvej/208rKypg9ezZ33303FovFaT/vvn37kpCQwJ49ewDYsmULq1evZujQoYDzftYVFRVUVlbi7e1d5biPjw+rV682fdwus1O1M7LZbEycOJHLLruMDh06AJCWloanp+cZN6MNDw8nLS3NhCprzrZt24iPj6ekpARfX1+++eYb2rVrR1JSktOO+YsvvuDnn3+uco39NGf9rHv37s2sWbNo3bo1x48f57nnnuPyyy9n+/btTjtmgAMHDvDee+8xadIk/va3v7Fx40b++te/4unpyZgxY+zj+/3u+s4w9tO+/fZbcnNzueuuuwDn/Tf+xBNPkJ+fT5s2bXBzc6OyspIXX3yRUaNGATjtZ+3n50d8fDwvvPACbdu2JTw8nM8//5zExERatGhh+rgViOqx8ePHs337dlavXm12KXWidevWJCUlkZeXx3/+8x/GjBnDypUrzS6r1hw+fJiHHnqIJUuWnPEblTM7/VsyQKdOnejduzcxMTF8+eWX+Pj4mFhZ7bLZbPTo0YN//OMfAHTt2pXt27czY8YMxowZY3J1deOjjz5i6NChREVFmV1Krfryyy+ZM2cOn332Ge3btycpKYmJEycSFRXl9J/1v//9b+6++24aN26Mm5sb3bp147bbbmPz5s1ml6ZVZvXVhAkTmD9/PsuXL6dJkyb24xEREZSVlZGbm1ulf3p6OhEREXVcZc3y9PSkRYsWdO/enWnTptG5c2feeustpx3z5s2bycjIoFu3bri7u+Pu7s7KlSt5++23cXd3Jzw83CnH/XuBgYG0atWKffv2Oe1nDRAZGUm7du2qHGvbtq39cuHp8f1+hZUzjB3g0KFDLF26lHvuucd+zFk/78cee4wnnniCkSNH0rFjR+68804efvhhpk2bBjj3Z928eXNWrlxJQUEBhw8fZsOGDZSXlxMXF2f6uBWI6hnDMJgwYQLffPMNy5YtIzY2tkp79+7d8fDwICEhwX4sOTmZ1NRU4uPj67rcWmWz2SgtLXXaMQ8YMIBt27aRlJRkf/To0YNRo0bZ/+yM4/69goIC9u/fT2RkpNN+1gCXXXbZGVto7Nmzh5iYGABiY2OJiIioMvb8/HzWr19f78cOMHPmTMLCwhg2bJj9mLN+3kVFRVitVX/8urm5YbPZAOf/rAEaNmxIZGQkOTk5LFq0iOuvv978cdf6tG2pUQ888IAREBBgrFixospS1aKiInuf+++/32jatKmxbNkyY9OmTUZ8fLwRHx9vYtWX7oknnjBWrlxppKSkGFu3bjWeeOIJw2KxGIsXLzYMwznHXJ3frjIzDOcc9yOPPGKsWLHCSElJMdasWWMMHDjQCAkJMTIyMgzDcM4xG8aprRXc3d2NF1980di7d68xZ84co0GDBsbs2bPtff75z38agYGBxnfffWds3brVuP76651iKXZlZaXRtGlTY/LkyWe0OePnPWbMGKNx48b2Zfdff/21ERISYjz++OP2Ps76WS9cuND48ccfjQMHDhiLFy82OnfubPTu3dsoKyszDMPccSsQ1TNAtY+ZM2fa+xQXFxt/+ctfjEaNGhkNGjQwbrzxRuP48ePmFV0D7r77biMmJsbw9PQ0QkNDjQEDBtjDkGE455ir8/tA5IzjvvXWW43IyEjD09PTaNy4sXHrrbdW2YvHGcd82vfff2906NDB8PLyMtq0aWO8//77VdptNpvxzDPPGOHh4YaXl5cxYMAAIzk52aRqa86iRYsMoNqxOOPnnZ+fbzz00ENG06ZNDW9vbyMuLs546qmnjNLSUnsfZ/2s586da8TFxRmenp5GRESEMX78eCM3N9febua4LYbxm60xRURERFyQ5hCJiIiIy1MgEhEREZenQCQiIiIuT4FIREREXJ4CkYiIiLg8BSIRERFxeQpEIiIi4vIUiERERMTlKRCJiIiIy1MgEhEREZenQCQiIiIuT4FIRJzSwoUL6devH4GBgQQHB3Pttdeyf/9+e/vatWvp0qUL3t7e9OjRg2+//RaLxUJSUpK9z/bt2xk6dCi+vr6Eh4dz5513cuLECRNGIyK1TYFIRJxSYWEhkyZNYtOmTSQkJGC1Wrnxxhux2Wzk5+dz3XXX0bFjR37++WdeeOEFJk+eXOX5ubm5XH311XTt2pVNmzaxcOFC0tPTueWWW0wakYjUJt3tXkRcwokTJwgNDWXbtm2sXr2ap59+miNHjuDt7Q3Ahx9+yL333ssvv/xCly5d+Pvf/86qVatYtGiR/TWOHDlCdHQ0ycnJtGrVyqyhiEgt0BkiEXFKe/fu5bbbbiMuLg5/f3+aNWsGQGpqKsnJyXTq1MkehgB69epV5flbtmxh+fLl+Pr62h9t2rQBqHLpTUScg7vZBYiI1IbrrruOmJgYPvjgA6KiorDZbHTo0IGysrLzen5BQQHXXXcdL7300hltkZGRNV2uiJhMgUhEnE5WVhbJycl88MEHXH755QCsXr3a3t66dWtmz55NaWkpXl5eAGzcuLHKa3Tr1o3//ve/NGvWDHd3fasUcXa6ZCYiTqdRo0YEBwfz/vvvs2/fPpYtW8akSZPs7bfffjs2m4377ruPXbt2sWjRIl599VUALBYLAOPHjyc7O5vbbruNjRs3sn//fhYtWsTYsWOprKw0ZVwiUnsUiETE6VitVr744gs2b95Mhw4dePjhh3nllVfs7f7+/nz//fckJSXRpUsXnnrqKaZMmQJgn1cUFRXFmjVrqKysZNCgQXTs2JGJEycSGBiI1apvnSLORqvMRESAOXPmMHbsWPLy8vDx8TG7HBGpY7owLiIu6dNPPyUuLo7GjRuzZcsWJk+ezC233KIwJOKiFIhExCWlpaUxZcoU0tLSiIyM5Oabb+bFF180uywRMYkumYmIiIjL08xAERERcXkKRCIiIuLyFIhERETE5SkQiYiIiMtTIBIRERGXp0AkIiIiLk+BSERERFyeApGIiIi4vP8PRrG5aUlw4N0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(population_df['age'], kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows:  48842\n",
      "Population Mean:  38.64358543876172\n"
     ]
    }
   ],
   "source": [
    "print('Number of rows: ', population_df.shape[0])\n",
    "print('Population Mean: ', population_df['age'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling in Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7762</th>\n",
       "      <td>56</td>\n",
       "      <td>Private</td>\n",
       "      <td>33115</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23881</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>112847</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30507</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>170525</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>14344</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28911</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>186788</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19484</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>277886</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43114</th>\n",
       "      <td>62</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>208266</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5042</th>\n",
       "      <td>45</td>\n",
       "      <td>Private</td>\n",
       "      <td>256866</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7027</th>\n",
       "      <td>25</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>170070</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36588</th>\n",
       "      <td>47</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>26145</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29572</th>\n",
       "      <td>59</td>\n",
       "      <td>Private</td>\n",
       "      <td>182062</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>5013</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4884 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt  education  educational-num  \\\n",
       "7762    56           Private   33115    HS-grad                9   \n",
       "23881   25           Private  112847    HS-grad                9   \n",
       "30507   43           Private  170525  Bachelors               13   \n",
       "28911   32           Private  186788    HS-grad                9   \n",
       "19484   39           Private  277886  Bachelors               13   \n",
       "...    ...               ...     ...        ...              ...   \n",
       "43114   62         Local-gov  208266  Assoc-voc               11   \n",
       "5042    45           Private  256866    HS-grad                9   \n",
       "7027    25         Local-gov  170070  Bachelors               13   \n",
       "36588   47  Self-emp-not-inc   26145  Assoc-voc               11   \n",
       "29572   59           Private  182062  Bachelors               13   \n",
       "\n",
       "           marital-status        occupation   relationship   race  gender  \\\n",
       "7762             Divorced     Other-service      Unmarried  White  Female   \n",
       "23881  Married-civ-spouse  Transport-moving      Own-child  Other    Male   \n",
       "30507            Divorced    Prof-specialty  Not-in-family  White  Female   \n",
       "28911  Married-civ-spouse  Transport-moving        Husband  White    Male   \n",
       "19484  Married-civ-spouse             Sales           Wife  White  Female   \n",
       "...                   ...               ...            ...    ...     ...   \n",
       "43114            Divorced      Adm-clerical  Not-in-family  Black  Female   \n",
       "5042   Married-civ-spouse    Prof-specialty        Husband  White    Male   \n",
       "7027        Never-married    Prof-specialty  Not-in-family  White  Female   \n",
       "36588  Married-civ-spouse             Sales        Husband  White    Male   \n",
       "29572  Married-civ-spouse    Prof-specialty        Husband  White    Male   \n",
       "\n",
       "       capital-gain  capital-loss  hours-per-week native-country income  \n",
       "7762              0             0              40  United-States  <=50K  \n",
       "23881             0             0              40  United-States  <=50K  \n",
       "30507         14344             0              40  United-States   >50K  \n",
       "28911             0             0              40  United-States  <=50K  \n",
       "19484             0             0              30  United-States  <=50K  \n",
       "...             ...           ...             ...            ...    ...  \n",
       "43114             0             0              40  United-States  <=50K  \n",
       "5042              0             0              45  United-States   >50K  \n",
       "7027              0             0              50  United-States   >50K  \n",
       "36588             0             0              40  United-States   >50K  \n",
       "29572          5013             0              40  United-States  <=50K  \n",
       "\n",
       "[4884 rows x 15 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Way 1 - Use frac to mention the fraction of data that you want to randomly sample\n",
    "\n",
    "population_df.sample(frac=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7762</th>\n",
       "      <td>56</td>\n",
       "      <td>Private</td>\n",
       "      <td>33115</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23881</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>112847</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30507</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>170525</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>14344</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28911</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>186788</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19484</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>277886</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2210</th>\n",
       "      <td>30</td>\n",
       "      <td>Private</td>\n",
       "      <td>267281</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32845</th>\n",
       "      <td>30</td>\n",
       "      <td>Private</td>\n",
       "      <td>272669</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36853</th>\n",
       "      <td>35</td>\n",
       "      <td>Private</td>\n",
       "      <td>202027</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>7298</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28697</th>\n",
       "      <td>57</td>\n",
       "      <td>Private</td>\n",
       "      <td>279636</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3792</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>186256</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age workclass  fnlwgt     education  educational-num  \\\n",
       "7762    56   Private   33115       HS-grad                9   \n",
       "23881   25   Private  112847       HS-grad                9   \n",
       "30507   43   Private  170525     Bachelors               13   \n",
       "28911   32   Private  186788       HS-grad                9   \n",
       "19484   39   Private  277886     Bachelors               13   \n",
       "...    ...       ...     ...           ...              ...   \n",
       "2210    30   Private  267281     Bachelors               13   \n",
       "32845   30   Private  272669  Some-college               10   \n",
       "36853   35   Private  202027     Bachelors               13   \n",
       "28697   57   Private  279636     Assoc-voc               11   \n",
       "3792    49   Private  186256  Some-college               10   \n",
       "\n",
       "           marital-status         occupation   relationship  \\\n",
       "7762             Divorced      Other-service      Unmarried   \n",
       "23881  Married-civ-spouse   Transport-moving      Own-child   \n",
       "30507            Divorced     Prof-specialty  Not-in-family   \n",
       "28911  Married-civ-spouse   Transport-moving        Husband   \n",
       "19484  Married-civ-spouse              Sales           Wife   \n",
       "...                   ...                ...            ...   \n",
       "2210        Never-married     Prof-specialty  Not-in-family   \n",
       "32845       Never-married       Tech-support  Not-in-family   \n",
       "36853  Married-civ-spouse  Handlers-cleaners        Husband   \n",
       "28697       Never-married       Craft-repair  Not-in-family   \n",
       "3792   Married-civ-spouse       Adm-clerical        Husband   \n",
       "\n",
       "                     race  gender  capital-gain  capital-loss  hours-per-week  \\\n",
       "7762                White  Female             0             0              40   \n",
       "23881               Other    Male             0             0              40   \n",
       "30507               White  Female         14344             0              40   \n",
       "28911               White    Male             0             0              40   \n",
       "19484               White  Female             0             0              30   \n",
       "...                   ...     ...           ...           ...             ...   \n",
       "2210                White    Male             0             0              50   \n",
       "32845  Asian-Pac-Islander    Male             0             0              40   \n",
       "36853               White    Male          7298             0              35   \n",
       "28697               White    Male             0             0              40   \n",
       "3792                White    Male             0             0              40   \n",
       "\n",
       "      native-country income  \n",
       "7762   United-States  <=50K  \n",
       "23881  United-States  <=50K  \n",
       "30507  United-States   >50K  \n",
       "28911  United-States  <=50K  \n",
       "19484  United-States  <=50K  \n",
       "...              ...    ...  \n",
       "2210   United-States  <=50K  \n",
       "32845  United-States  <=50K  \n",
       "36853  United-States   >50K  \n",
       "28697  United-States   >50K  \n",
       "3792   United-States  <=50K  \n",
       "\n",
       "[100 rows x 15 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Way 2 - Use n to mention the number of datapoints that you want to randomly sample\n",
    "\n",
    "population_df.sample(n=100, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Point Estimate using 1 Sample\n",
    "Point estimation involves using a single value (a point) from the sample data to estimate the population parameter. For example, using the sample mean to estimate the population mean. \n",
    "\n",
    "**Step 1 -** Take a large enough uniform random sample of size n.  \n",
    "**Step 2 -** Calculate the Sample Statistics: $ \\bar{x} = \\frac{\\sum{data_i}}{n} $.  \n",
    "**Step 3 -** Estimate the Population Parameter using the Sample Statistics: $ \\mu_{POP} \\approx \\bar{x}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9768, 15)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1 - Uniformly at random take a large enough sample from population\n",
    "\n",
    "sample_df = population_df.sample(frac=0.2, random_state=42)\n",
    "\n",
    "sample_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.7273751023751"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 2 - Calculate the Sample Statistics\n",
    "\n",
    "sample_df['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population Mean (i.e. Parameter): 38.64358543876172\n",
      "Sample Mean (i.e. Statistics): 38.7273751023751\n",
      "\n",
      "R E S U L T\n",
      "Population mean is estimated to be approximately equal to 38.7273751023751.\n"
     ]
    }
   ],
   "source": [
    "# # Step 3 - Estimate the Population Parameter using the Sample Statistics\n",
    "\n",
    "# # As we have a complete population data - \n",
    "# # Let's check what is the actual population parameter.\n",
    "\n",
    "print(\"Population Mean (i.e. Parameter):\", population_df['age'].mean())\n",
    "print(\"Sample Mean (i.e. Statistics):\", sample_df['age'].mean())\n",
    "\n",
    "print()\n",
    "print(\"R E S U L T\")\n",
    "print(f\"Population mean is estimated to be approximately equal to {sample_df['age'].mean()}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How large a sample should be?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>24.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>54.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n       mean\n",
       "0  1  26.000000\n",
       "1  2  35.000000\n",
       "2  3  37.333333\n",
       "3  4  24.750000\n",
       "4  5  54.800000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_means = []\n",
    "\n",
    "for sample_size in np.arange(1, 1000):\n",
    "    sample_df = population_df.sample(n=sample_size)\n",
    "    sample_means.append((sample_size, sample_df['age'].mean()))\n",
    "\n",
    "sample_means_df = pd.DataFrame(sample_means, columns=['n', 'mean'])\n",
    "\n",
    "sample_means_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 4))\n",
    "\n",
    "ax.set_title(\"How large a sample should be?\")  # Add a title to the axes\n",
    "ax.set_xlabel('Sample Size')  # Add an x-label to the axes\n",
    "ax.set_ylabel('Sample Mean')  # Add a y-label to the axes\n",
    "\n",
    "sns.lineplot(data=sample_means_df, x=\"n\", y=\"mean\", ax=ax, label=\"Sample Mean\")\n",
    "ax.axhline(population_df['age'].mean(), ls='--', color='red', label=\"Population Mean\")\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Take a sample (Large enough)\n",
    "sample_df = population_df.sample(n=200, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.94"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 2: Calculate sample statistic\n",
    "sample_df['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population Mean (i.e. Parameter): 38.64358543876172\n",
      "Sample Mean (i.e. Statistics): 38.94\n",
      "\n",
      "R E S U L T\n",
      "Population mean is estimated to be approximately equal to 38.94.\n"
     ]
    }
   ],
   "source": [
    "# Step 3: Pop Mean approx equal to Sample statistic\n",
    "\n",
    "# # As we have a complete population data - \n",
    "# # Let's check what is the actual population parameter.\n",
    "\n",
    "print(\"Population Mean (i.e. Parameter):\", population_df['age'].mean())\n",
    "print(\"Sample Mean (i.e. Statistics):\", sample_df['age'].mean())\n",
    "\n",
    "print()\n",
    "print(\"R E S U L T\")\n",
    "print(f\"Population mean is estimated to be approximately equal to {sample_df['age'].mean()}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Central Limit Theorem\n",
    "\n",
    "The Central Limit Theorem (CLT) is a fundamental concept in statistics that describes the behavior of the sampling distribution of the sample mean as the sample size gets larger, **regardless of the shape of the population distribution**.\n",
    "\n",
    "The Central Limit Theorem allows us to make reliable inferences about a population based on sample data, assuming that Sample size is sufficiently large (typically around 30 or more). It doesn't necessarily apply to other sample statistics like medians or variances. \n",
    "\n",
    "The CLT is widely used in various fields, such as quality control in manufacturing, finance, hypothesis testing, and in the interpretation of experimental results.\n",
    "\n",
    "According to Central Limit Theorem, the sampling distribution of the sample mean $\\bar{X}$:\n",
    "1. Will be approximately normally distributed, regardless of the shape of the original population distribution.\n",
    "\\begin{equation}\n",
    "\\ \\bar{X} \\sim N(\\mu_\\bar{X}, \\sigma_\\bar{X})\n",
    "\\end{equation}\n",
    "2. Will have a mean that is equal to the population mean (i.e. Population Parameter).\n",
    "\\begin{equation}\n",
    "\\ \\mu_\\bar{X} \\approx \\mu_{pop}\n",
    "\\end{equation}\n",
    "3. Will have a standard deviation that is equal to the population standard deviation divided by the square root of the sample size\n",
    "\\begin{equation}\n",
    "\\ \\sigma_\\bar{X} \\approx \\frac{\\sigma_{pop}}{\\sqrt{n}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sampling_distribution(data, sample_size, number_of_sample):\n",
    "    sample_means = []\n",
    "    for m in range(number_of_sample):\n",
    "        sample = data.sample(n=sample_size)\n",
    "        sample_means.append((sample_size, sample.mean()))\n",
    "    sampling_distribution_df = pd.DataFrame(sample_means, columns=['n', 'mean'])\n",
    "\n",
    "    print(\"*\"*20, \" R E P O R T \", \"*\"*20)\n",
    "    print(\"Mean Check\")\n",
    "    print(\"Sampling Distribution Mean:\", sampling_distribution_df[\"mean\"].mean())\n",
    "    print(\"Population Mean: \", data.mean())\n",
    "\n",
    "    print()\n",
    "    print(\"Standard Deviation Check\")\n",
    "    print(\"Sampling Distribution Std:\", sampling_distribution_df[\"mean\"].std())\n",
    "    print(\"Population Std / (sample_size)**0.5:\", data.std()/np.sqrt(sample_size))\n",
    "\n",
    "    print(\"*\"*55)\n",
    "    \n",
    "    return sampling_distribution_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sampling_distribution_plot(data):\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(8, 3), constrained_layout=True)\n",
    "    \n",
    "    axes[0].set_title(\"Sampling Distribution\")  # Add a title to the axes\n",
    "    axes[0].set_xlabel('Mean')  # Add an x-label to the axes\n",
    "    axes[0].set_ylabel('Density')  # Add a y-label to the axes\n",
    "\n",
    "    sns.histplot(data, kde=True, ax=axes[0])\n",
    "    \n",
    "    stats.probplot(data, dist=stats.norm, plot=axes[1])\n",
    "    axes[1].grid()\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************  R E P O R T  ********************\n",
      "Mean Check\n",
      "Sampling Distribution Mean: 38.3944\n",
      "Population Mean:  38.64358543876172\n",
      "\n",
      "Standard Deviation Check\n",
      "Sampling Distribution Std: 2.1189745566469576\n",
      "Population Std / (sample_size)**0.5: 1.9389589096341133\n",
      "*******************************************************\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=50\n",
    "m=100\n",
    "\n",
    "sampling_distribution_df = sampling_distribution(data=population_df['age'], sample_size=n, number_of_sample=m)\n",
    "\n",
    "sampling_distribution_plot(data=sampling_distribution_df[\"mean\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Point Estimate using m Sample\n",
    "\n",
    "According to Central Limit Theorem, the sampling distribution of the sample mean will have a mean that is equal to the population mean.\n",
    "\n",
    "**Step 1 -** Take m different large enough uniform random sample of size n.  \n",
    "**Step 2 -** Calculate the sample statistics for each sample: $ \\bar{x_i} = \\frac{\\sum{data}}{n} $  \n",
    "**Step 3 -** According to Central Limit Theorem:  \n",
    "    a. You will get a Sampling Distribution of Sample Mean: $ \\bar{X} = [\\bar{x_1}, \\bar{x_2}, \\bar{x_3}, ... , \\bar{x_m}] $  \n",
    "    b. Calculate the Mean of Sampling Distribution: $ \\mu_\\bar{X} = \\frac{\\sum{\\bar{x_i}}}{m} $   \n",
    "    c. As per CLT, we know that: $ \\mu_\\bar{X} \\approx \\mu_{POP} $     \n",
    "**Step 4 -** Estimate the Population Parameter using the Mean of Sample Statistics: $ \\mu_{POP} \\approx \\mu_\\bar{X}$\n",
    "\n",
    "**But, is it really a good idea to collect these many samples?**  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************  R E P O R T  ********************\n",
      "Mean Check\n",
      "Sampling Distribution Mean: 38.65638\n",
      "Population Mean:  38.64358543876172\n",
      "\n",
      "Standard Deviation Check\n",
      "Sampling Distribution Std: 1.8811039970846501\n",
      "Population Std / (sample_size)**0.5: 1.9389589096341133\n",
      "*******************************************************\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "R E S U L T\n",
      "Population mean is estimated to be approximately equal to 38.65638.\n"
     ]
    }
   ],
   "source": [
    "n=50\n",
    "m=1000\n",
    "\n",
    "sampling_distribution_df = sampling_distribution(data=population_df['age'], sample_size=n, number_of_sample=m)\n",
    "\n",
    "sampling_distribution_plot(data=sampling_distribution_df[\"mean\"])\n",
    "\n",
    "print()\n",
    "print(\"R E S U L T\")\n",
    "print(f\"Population mean is estimated to be approximately equal to {sampling_distribution_df['mean'].mean()}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confidence Interval Estimate using 1 Sample (if population std is known)\n",
    "\n",
    "Confidence interval estimation provides a range (interval) of values within which the true population parameter is likely to fall. **It involves calculating a confidence interval around the point estimate.** For example, a 95% confidence interval might be [160 cm, 170 cm], indicating that we are 95% confident that the true population mean height falls within this range.\n",
    "\n",
    "**Step 1 -** Take a large enough uniform random sample of size n.  \n",
    "**Step 2 -** Calculate the sample statistics: $ \\bar{x} = \\frac{\\sum{data}}{n} $  \n",
    "In order for this to work, **REMEMBER** the following:  \n",
    "    a. $ \\bar{x} $ comes from a Sampling Distribution i.e. $ \\bar{X} $   \n",
    "    b. We should know the Population's Standard Deviation i.e. $ \\sigma_{POP} $   \n",
    "    c. This is because, Sampling Distribution's Standard Deviation or Standard Error i.e. $ \\sigma_{\\bar{X}} = \\frac{\\sigma_{POP}}{\\sqrt{n}} $   \n",
    "**Step 3 -** Now set the confidence level and **CALCULATE** the margin of error as following:  \n",
    "    a. Calculate the Standard Error (SE) $ \\sigma_{\\bar{X}} = \\frac{\\sigma_{POP}}{\\sqrt{n}} $   \n",
    "    b. Calculate the critical value (z-score).  \n",
    "    c. Calculate the margin of error (MOE = z_score * SE).  \n",
    "**Step 4 -** Estimate the Population Parameter using sample mean and population standard deviation:  \n",
    "$$ \\mu_{POP} \\approx [\\bar{x} \\pm (z^*_\\frac{\\alpha}{2} * \\frac{\\sigma_{POP}}{\\sqrt{n}})] with \\ y\\% \\ confidence $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Take a sample (Large enough)\n",
    "sample_size = 200\n",
    "\n",
    "sample_df = population_df.sample(n=sample_size, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population Std: 13.71036957798689\n",
      "Sample Mean: 38.94\n",
      "Sample Std: 13.512934432791592\n"
     ]
    }
   ],
   "source": [
    "# Step 2: Calculate sample statistics (i.e. mean and standard deviation of the sample)\n",
    "\n",
    "population_std = np.std(population_df['age'])\n",
    "sample_mean = np.mean(sample_df['age'])\n",
    "sample_std = np.std(sample_df['age'], ddof=1)\n",
    "\n",
    "# Use ddof=1 for sample standard deviation\n",
    "# The divisor used in calculations is N - ddof, where N represents the number of elements. \n",
    "# By default ddof is zero.\n",
    "\n",
    "print(\"Population Std:\", population_std)\n",
    "print(\"Sample Mean:\", sample_mean)\n",
    "print(\"Sample Std:\", sample_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3: Set confidence level (e.g., 95% confidence level)\n",
    "confidence_level = 0.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard Error: 0.9694695301168273\n"
     ]
    }
   ],
   "source": [
    "# Step 3a: Calculate standard error (SE)\n",
    "standard_error = population_std / np.sqrt(sample_size)\n",
    "\n",
    "print(\"Standard Error:\", standard_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z Score: 1.959963984540054\n"
     ]
    }
   ],
   "source": [
    "# Step 3b: Calculate the critical value (z-score)\n",
    "significance_level = 1 - confidence_level\n",
    "\n",
    "z_score = stats.norm.ppf(1 - significance_level/2)\n",
    "\n",
    "print(\"Z Score:\", z_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Margin of Error: 1.9001253631379509\n"
     ]
    }
   ],
   "source": [
    "# Step 3c: Calculate the margin of error (MOE)\n",
    "margin_of_error = z_score * standard_error\n",
    "\n",
    "print(\"Margin of Error:\", margin_of_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lower Bound: 37.039874636862045\n",
      "Upper Bound: 40.84012536313795\n"
     ]
    }
   ],
   "source": [
    "# Step 4: Calculate the confidence interval\n",
    "lower_bound = sample_mean - margin_of_error\n",
    "upper_bound = sample_mean + margin_of_error\n",
    "\n",
    "print(\"Lower Bound:\", lower_bound)\n",
    "print(\"Upper Bound:\", upper_bound)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Mean: 38.94\n",
      "Margin of Error: 1.9001253631379509\n",
      "Confidence Interval (95.0%): (37.039874636862045, 40.84012536313795)\n",
      "\n",
      "R E S U L T\n",
      "Population mean is estimated to lie in (37.039874636862045, 40.84012536313795) range with 95.0% confidence.\n"
     ]
    }
   ],
   "source": [
    "# Print the results\n",
    "print(f\"Sample Mean: {sample_mean}\")\n",
    "print(f\"Margin of Error: {margin_of_error}\")\n",
    "print(f\"Confidence Interval ({confidence_level*100}%): ({lower_bound}, {upper_bound})\")\n",
    "\n",
    "print()\n",
    "print(\"R E S U L T\")\n",
    "print(f\"Population mean is estimated to lie in ({lower_bound}, {upper_bound}) range with {confidence_level*100}% confidence.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explanation:**\n",
    "1. Take a sufficiently large enough sample dataset.\n",
    "2. Set the desired confidence level (e.g., 95% confidence level).\n",
    "3. Calculate the sample mean, sample standard deviation, standard error, and the margin of error.\n",
    "4. Using the z-distribution, we find the critical value (z-score) corresponding to the desired confidence level.\n",
    "5. Calculate the lower and upper bounds of the confidence interval.\n",
    "6. Finally, we print out the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confidence Interval Estimate using 1 Sample (if Population Std is unknown)\n",
    "\n",
    "**Step 1 -** Take a uniform random sample of size n.  \n",
    "**Step 2 -** Calculate the sample statistics: $ \\bar{x} = \\frac{\\sum{data}}{n} $  \n",
    "In order for this to work, **REMEMBER** the following:  \n",
    "    a. $ \\bar{x} $ comes from a Sampling Distribution i.e. $ \\bar{X} $   \n",
    "    b. In this case Population Standard Deviation is UNKNOWN.     \n",
    "**Step 3 -** Now set the confidence level and **CALCULATE** the margin of error as following:  \n",
    "    a. Calculate the Standard Error (SE) $ \\sigma_{\\bar{X}} = \\frac{S}{\\sqrt{n}} $   \n",
    "    b. Calculate the critical value (t-score).  \n",
    "    c. Calculate the margin of error (MOE = t_score * SE).  \n",
    "**Step 4 -** Estimate the Population Parameter using sample mean and population standard deviation:  \n",
    "$$ \\mu_{POP} \\approx [\\bar{x} \\pm (t^*_{\\frac{\\alpha}{2}, n-1} * \\frac{S}{\\sqrt{n}})] with \\ y\\% \\ confidence $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Take a sample\n",
    "sample_size = 200\n",
    "\n",
    "sample_df = population_df.sample(n=sample_size, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Mean: 38.94\n",
      "Sample Std: 13.512934432791592\n"
     ]
    }
   ],
   "source": [
    "# Step 2: Calculate sample statistics (i.e. mean and standard deviation of the sample)\n",
    "\n",
    "sample_mean = np.mean(sample_df['age'])\n",
    "sample_std = np.std(sample_df['age'], ddof=1)\n",
    "\n",
    "# Use ddof=1 for sample standard deviation\n",
    "# The divisor used in calculations is N - ddof, where N represents the number of elements. \n",
    "# By default ddof is zero.\n",
    "\n",
    "print(\"Sample Mean:\", sample_mean)\n",
    "print(\"Sample Std:\", sample_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3: Set confidence level (e.g., 95% confidence level)\n",
    "confidence_level = 0.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard Error: 0.9555087571156128\n"
     ]
    }
   ],
   "source": [
    "# Step 3a: Calculate standard error (SE)\n",
    "standard_error = sample_std / np.sqrt(sample_size)\n",
    "\n",
    "print(\"Standard Error:\", standard_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t Score: 1.971956544249395\n"
     ]
    }
   ],
   "source": [
    "# Step 3b: Calculate the critical value (z-score)\n",
    "significance_level = 1 - confidence_level\n",
    "\n",
    "t_score = stats.t.ppf(1 - significance_level/2, df=sample_size-1)\n",
    "\n",
    "print(\"t Score:\", t_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Margin of Error: 1.8842217466817381\n"
     ]
    }
   ],
   "source": [
    "# Step 3c: Calculate the margin of error (MOE)\n",
    "margin_of_error = t_score * standard_error\n",
    "\n",
    "print(\"Margin of Error:\", margin_of_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lower Bound: 37.05577825331826\n",
      "Upper Bound: 40.82422174668174\n"
     ]
    }
   ],
   "source": [
    "# Step 4: Calculate the confidence interval\n",
    "lower_bound = sample_mean - margin_of_error\n",
    "upper_bound = sample_mean + margin_of_error\n",
    "\n",
    "print(\"Lower Bound:\", lower_bound)\n",
    "print(\"Upper Bound:\", upper_bound)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Mean: 38.94\n",
      "Margin of Error: 1.8842217466817381\n",
      "Confidence Interval (95.0%): (37.05577825331826, 40.82422174668174)\n",
      "\n",
      "R E S U L T\n",
      "Population mean lies in (37.05577825331826, 40.82422174668174) range with 95.0% confidence.\n"
     ]
    }
   ],
   "source": [
    "# Print the results\n",
    "print(f\"Sample Mean: {sample_mean}\")\n",
    "print(f\"Margin of Error: {margin_of_error}\")\n",
    "print(f\"Confidence Interval ({confidence_level*100}%): ({lower_bound}, {upper_bound})\")\n",
    "\n",
    "print()\n",
    "print(\"R E S U L T\")\n",
    "print(f\"Population mean lies in ({lower_bound}, {upper_bound}) range with {confidence_level*100}% confidence.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explanation:**\n",
    "1. Take a sufficiently large enough sample dataset.\n",
    "2. Set the desired confidence level (e.g., 95% confidence level).\n",
    "3. Calculate the sample mean, sample standard deviation, standard error, and the margin of error.\n",
    "4. Using the t-distribution (since the sample size is small), we find the critical value (t-score) corresponding to the desired confidence level.\n",
    "5. Calculate the lower and upper bounds of the confidence interval.\n",
    "6. Finally, we print out the results."
   ]
  }
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